Version 1.5

Legend for changelogs

• Major Feature something big that you couldn’t do before.

• Feature something that you couldn’t do before.

• Efficiency an existing feature now may not require as much computation or memory.

• Enhancement a miscellaneous minor improvement.

• Fix something that previously didn’t work as documented – or according to reasonable expectations – should now work.

• API Change you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

Version 1.5.0

In Development

Support for building with Meson

Meson is now supported as a build backend, see Building with Meson for more details.

#28040 by Loïc Estève

TODO Fill more details before the 1.5 release, when the Meson story has settled down.

Metadata Routing

The following models now support metadata routing in one or more or their methods. Refer to the Metadata Routing User Guide for more details.

• Feature impute.IterativeImputer now supports metadata routing in its fit method. #28187 by Stefanie Senger.

Changelog

sklearn.compose

• Feature A fitted compose.ColumnTransformer now implements __getitem__ which returns the fitted transformers by name. #27990 by Thomas Fan.

sklearn.dummy

• Enhancement dummy.DummyClassifier and dummy.DummyRegressor now have the n_features_in_ and feature_names_in_ attributes after fit. #27937 by Marco vd Boom.

sklearn.ensemble

• Efficiency Improves runtime of predict of ensemble.HistGradientBoostingClassifier by avoiding to call predict_proba. #27844 by Christian Lorentzen.

• Efficiency ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor are now a tiny bit faster by pre-sorting the data before finding the thresholds for binning. #28102 by Christian Lorentzen.

sklearn.feature_extraction

• Efficiency feature_extraction.text.TfidfTransformer is now faster and more memory-efficient by using a NumPy vector instead of a sparse matrix for storing the inverse document frequency. #18843 by Paolo Montesel.

• Enhancement feature_extraction.text.TfidfTransformer now preserves the data type of the input matrix if it is np.float64 or np.float32. #28136 by Guillaume Lemaitre.

sklearn.feature_selection

• Enhancement feature_selection.mutual_info_regression and feature_selection.mutual_info_classif now support n_jobs parameter. #28085 by Neto Menoci and Florin Andrei.

sklearn.impute

• Enhancement impute.SimpleImputer now supports custom strategies by passing a function in place of a strategy name. #28053 by Mark Elliot.

sklearn.metrics

• Efficiency Improve efficiency of functions brier_score_loss, calibration_curve, det_curve, precision_recall_curve, roc_curve when pos_label argument is specified. Also improve efficiency of methods from_estimator and from_predictions in RocCurveDisplay, PrecisionRecallDisplay, DetCurveDisplay, CalibrationDisplay. #28051 by Pierre de Fréminville.

• API Change metrics.precision_recall_curve deprecated the keyword argument probas_pred in favor of y_score. probas_pred will be removed in version 1.7. #28092 by Adam Li.

• API Change metrics.brier_score_loss deprecated the keyword argument y_prob in favor of y_proba. y_prob will be removed in version 1.7. #28092 by Adam Li.

sklearn.model_selection

• Enhancement CV splitters that ignores the group parameter now raises a warning when groups are passed in to split. #28210 by

sklearn.pipeline

• Feature pipeline.FeatureUnion can now use the verbose_feature_names_out attribute. If True, get_feature_names_out will prefix all feature names with the name of the transformer that generated that feature. If False, get_feature_names_out will not prefix any feature names and will error if feature names are not unique. #25991 by Jiawei Zhang.

Code and documentation contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.4, including:

TODO: update at the time of the release.